This analysis examines two major interventions that reshaped the sports betting landscape: the COVID-19 pandemic (March 2020) and New York’s online sports betting legalization (January 2022). Using Google Trends data spanning 2020-2024, we employ interrupted time series regression to quantify both immediate shocks and sustained trend changes. Google Trends provides a direct cultural proxy for public engagement, capturing how these events transformed sports betting from a niche activity during COVID’s sports shutdown to mainstream entertainment following New York’s market entry. The ITS model
Intervention: New York online sports betting launch (January 8, 2022) - the largest U.S. sports betting market by revenue, representing ~20% of the national market.
Research Question: Did NY legalization produce a significant change in public interest in sports betting?
We analyze Google search trends as a direct measure of cultural impact and consumer awareness, examining four search terms that capture different facets of betting interest from general (“sports betting”) to operator-specific (“draftkings”) to sport-specific (“nba betting”).
Visualization & Data Preparation
Code
p1 <-ggplot(sports_betting, aes(x = date, y = hits)) +geom_line(color ="#1e88e5", linewidth =1) +geom_vline(xintercept = intervention_date, color ="red", linetype ="dashed", linewidth =1.2) +annotate("text",x = intervention_date, y =max(sports_betting$hits) *0.95,label ="NY Launch\nJan 8, 2022", color ="red", size =4, fontface ="bold" ) +labs(title ="Google Searches: 'sports betting'",subtitle ="NY sports betting legalization intervention point marked in red",x ="Date", y ="Search Interest (0-100)" ) +theme(plot.title =element_text(face ="bold"))p2 <-ggplot(draftkings, aes(x = date, y = hits)) +geom_line(color ="#ff6f00", linewidth =1) +geom_vline(xintercept = intervention_date, color ="red", linetype ="dashed", linewidth =1.2) +annotate("text",x = intervention_date, y =max(draftkings$hits) *0.95,label ="NY Launch\nJan 8, 2022", color ="red", size =4, fontface ="bold" ) +labs(title ="Google Searches: 'draftkings'", x ="Date", y ="Search Interest (0-100)") +theme(plot.title =element_text(face ="bold"))p3 <-ggplot(online_betting, aes(x = date, y = hits)) +geom_line(color ="#43a047", linewidth =1) +geom_vline(xintercept = intervention_date, color ="red", linetype ="dashed", linewidth =1.2) +annotate("text",x = intervention_date, y =max(online_betting$hits) *0.95,label ="NY Launch\nJan 8, 2022", color ="red", size =4, fontface ="bold" ) +labs(title ="Google Searches: 'online betting'", x ="Date", y ="Search Interest (0-100)") +theme(plot.title =element_text(face ="bold"))p4 <-ggplot(nba_betting, aes(x = date, y = hits)) +geom_line(color ="#8e24aa", linewidth =1) +geom_vline(xintercept = intervention_date, color ="red", linetype ="dashed", linewidth =1.2) +annotate("text",x = intervention_date, y =max(nba_betting$hits) *0.95,label ="NY Launch\nJan 8, 2022", color ="red", size =4, fontface ="bold" ) +labs(title ="Google Searches: 'nba betting'", x ="Date", y ="Search Interest (0-100)") +theme(plot.title =element_text(face ="bold"))ny_trends_grid <- gridExtra::grid.arrange(p1, p2, p3, p4, ncol =2)
Code
# Save for presentationggsave("images/ny_betting_trends.png", plot = ny_trends_grid, width =14, height =10, dpi =300, bg ="white")# Data preparation tableprep_table <- sports_betting %>%filter(date >=as.Date("2021-12-01") & date <=as.Date("2022-02-28")) %>%select(date, hits, Time, Post_Intervention, Time_Since_Intervention) %>%mutate(Period =ifelse(Post_Intervention ==0, "Pre-NY", "Post-NY")) %>%select(date, Period, Y = hits, X_t = Time, Z_t = Post_Intervention, P_t = Time_Since_Intervention)kable(prep_table,format ="html", digits =1,caption ="ITS Variables for NY Legalization Analysis (Sample Period)",col.names =c("Date", "Period", "Y (Search Interest)", "X_t (Time)", "Z_t (NY Indicator)", "P_t (Time Since NY)")) %>%kable_styling(full_width =FALSE, bootstrap_options =c("striped", "hover", "condensed")) %>%row_spec(0, bold =TRUE, background ="#2c3e50", color ="white") %>%row_spec(which(prep_table$Z_t ==1), background ="#e3f2fd")
ITS Variables for NY Legalization Analysis (Sample Period)
β₁ (Pre-Trend) = 0.0316 (p = 0.0000): Search interest increased by 0.0316 points per week before NY launch (significant)
β₂ (Level Change) = -9.3448 (p = 0.0002): Significant immediate drop of 9.34 points when NY launched (intervention had immediate effect)
β₃ (Trend Change) = -0.0255 (p = 0.0000): Post-NY trend = 0.0316 (pre) + -0.0255 (change) = 0.0061 per week Deceleration in growth rate after NY launch (significant sustained effect)
Average Effect (Post-NY): -23.21 points
Current Effect: -37.05 points
Counterfactual: Without NY legalization, current search interest would be 72.0 instead of 34.0
The β₂ coefficient captures the immediate effect, indicating whether search interest jumped or declined right after New York’s January 8, 2022 legalization, with a significant negative value signaling an immediate cultural response. The counterfactual blue dashed line shows what search interest would have been without the intervention, and the gap between it and the actual red line quantifies the overall impact at any point. The β₃ coefficient reflects the sustained effect by measuring how the growth trajectory changed post-legalization, where negative values indicate slowing interest as the market stabilized. Together with the delayed effect, intervention difference and the ongoing separation between actual and counterfactual, these patterns reveal how the impact evolved and persisted well beyond the initial launch period.
Background & Variable Selection
Intervention: WHO declared COVID-19 pandemic on March 11, 2020, causing immediate suspension of all major sports leagues (NBA, NHL, MLB).
Variable: “Sports betting” Google searches - an ITS candidate because the intervention had a clear, direct causal pathway: no live sports = no sports betting.
β₁ (Pre-COVID Trend) = -1.2091 (p = 0.2236): Search interest decreased by 1.2091 points per week before pandemic
β₂ (Immediate Shock) = 7.8733 (p = 0.2171): Search interest DROPPED by 7.87 points when sports stopped. This represents the immediate collapse in betting interest when all sports leagues shut down
β₃ (Recovery Rate) = 1.2636 (p = 0.2035): Post-COVID trend = -1.2091 (pre) + 1.2636 (change) = 0.0546 per week Trend became MORE POSITIVE, indicating RECOVERY as sports returned
Average Effect (Post-COVID): 166.55 points below counterfactual
Counterfactual: Without COVID, current search interest would be -288.0 instead of 34.0
Code
ggplot(post_covid_data, aes(x = date, y = Effect)) +geom_line(color ="#d32f2f", linewidth =1.2) +geom_point(color ="#d32f2f", size =2) +geom_hline(yintercept =0, linetype ="dashed", color ="gray30", linewidth =1) +geom_smooth(method ="loess", se =TRUE, color ="#1e88e5", fill ="#1e88e5", alpha =0.2) +labs(title ="Intervention Effect Over Time: COVID-19 Impact Trajectory",subtitle ="Negative values = Interest below counterfactual | Positive = Above counterfactual",x ="Date", y ="Intervention Effect (Predicted - Counterfactual)",caption ="Blue trend line shows recovery trajectory over time" ) +theme(plot.title =element_text(face ="bold", size =15))
β₂ captures the immediate effect, showing that search interest dropped roughly %.2 points when sports shut down, a catastrophic collapse that erased the core driver of betting demand, while the counterfactual trajectory shows what interest would have been without COVID, with the gap representing “lost interest,” averaging %.1 points below the expected level. β₃ reflects the sustained effect, revealing how the trend shifted as sports gradually returned with the NBA bubble and NFL season, producing a delayed pattern of sharp collapse followed by slow recovery. The current distance between the actual and counterfactual lines indicates whether the industry has fully realigned with its pre-pandemic path or if meaningful deficits persist.